Machine learning books and papers

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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

📈 Analytical overview of Telegram channel Machine learning books and papers

Channel Machine learning books and papers (@machine_learn) in the English language segment is an active participant. Currently, the community unites 24 517 subscribers, ranking 8 056 in the Education category and 13 757 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 517 subscribers.

According to the latest data from 24 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -165 over the last 30 days and by -3 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.78%. Within the first 24 hours after publication, content typically collects 1.90% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 663 views. Within the first day, a publication typically gains 465 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
Thematic interests: Content is focused on key topics such as disorder, psy, مقاله, framework, graph.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn”

Thanks to the high frequency of updates (latest data received on 25 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

24 517

Subscribers

-324 hours

-477 days

-16530 days

1 663

Post views

~ 46524 hours

~ 57948 hours

6.78%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 518

🔹 Title: Predicting the Order of Upcoming Tokens Improves Language Modeling 🔹 Publication Date: Published on Aug 26 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.19228 • PDF: https://arxiv.org/pdf/2508.19228 • Github: https://github.com/zaydzuhri/token-order-prediction @Machine_learn

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🔹 Title: CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning 🔹 Publication Date: Published on Aug 27 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.20096 • PDF: https://arxiv.org/pdf/2508.20096 • Project Page: https://github.com/OpenIXCLab/CODA • Github: https://github.com/OpenIXCLab/CODA @Machine_learn

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رمضان الکریم ❤️ @Machine_learn

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Dataset Name: Gallstone Dataset (UCI) Basic Description: Gallstone Dataset (UCI Machine Learning Repository) 📥 DATASET DOWNLOAD INFORMATION ================================== 🔴 Dataset Size: Download dataset as zip (81 kB) 🔰 Direct dataset download link: URL not found 📊 Additional information: ================================== File count not found Views: 1,128 Downloads: 246 📚 RELATED NOTEBOOKS: ================================== 1. Heart Attack Risk Prediction Dataset | Upvotes: 274 URL: https://www.kaggle.com/datasets/iamsouravbanerjee/heart-attack-prediction-dataset @Machine_learn

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How we made Python's packaging library 3x faster 📚 Read @Machine_learn

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با عرض سلام برای مقاله زیر نیاز به نفرات ۲ و ۳ داریم. KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5. KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs. We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively. .... Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment 2 :20 milion 3 :15 milion @Raminmousa @Machine_learn @paper4money

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🔹 Title: Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents 🔹 Publication Date: Published on Aug 27 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.19493 • PDF: https://arxiv.org/pdf/2508.19493 • Project Page: https://zhixin-l.github.io/SAPA-Bench • Github: https://github.com/Zhixin-L/SAPA-Bench @Machine_learn

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🔹 Title: Self-Rewarding Vision-Language Model via Reasoning Decomposition 🔹 Publication Date: Published on Aug 27 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.19652 • PDF: https://arxiv.org/pdf/2508.19652 @Machine_learn

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🚀 AI Agents for Android Apps 📌 GitHub: https://github.com/actionstatelabs/android-action-kernel @Machine_learn

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سلام اين مقاله امشب سابميت ميشه اگر از دوستان كسي نياز داشت با من هماهنگ بشه @Raminmousa

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با عرض سلام ما برای این مقاله نیاز به نفر دوم داریم و تنها مقاله دو نفر جایگاه داره. دوستانی که نیاز دارن می تونن به پی وی بنده پیام بدن @Raminmousa ⚠️ فردا اخرین مهلت ...!

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با عرض سلام ما برای این مقاله نیاز به نفر دوم داریم و تنها مقاله دو نفر جایگاه داره. دوستانی که نیاز دارن می تونن به پی وی بنده پیام بدن @Raminmousa

24 518

Repost from Papers

Title: Fundamental Challenges of Neural Network in Handling Sequential Feature of Time Series: Np-hard Challenge Journal: IEEE transaction on soft computing Author : 2 Price: 1200 USDT @Raminmousa @Machine_learn @Paper4money

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🔹 Title: Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering 🔹 Publication Date: Published on Aug 21 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.15213 • PDF: https://arxiv.org/pdf/2508.15213 @Machine_learn

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Sharing State Between Prompts and Programs 📚 Read @Machine_learn

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Dataset Name: Online Payments Fraud Detection Dataset Basic Description: Online payment fraud big dataset for testing and practice purpose 📖 FULL DATASET DESCRIPTION: The below column reference: 📥 DATASET DOWNLOAD INFORMATION 🔴 Dataset Size: Download dataset as zip (186 MB) 🔰 Direct dataset download link: https://www.kaggle.com/api/v1/datasets/download/rupakroy/online-payments-fraud-detection-dataset @Machine_learn

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Dataset Name: Linked In Job Postings (2023 - 2024) Basic Description: LinkedIn Job Postings (2023 - 2024) 📖 FULL DATASET DESCRIPTION: Scraper Code - https://github.com/ArshKA/LinkedIn-Job-Scraper Every day, thousands of companies and individuals turn to LinkedIn in search of talent. This dataset contains a nearly comprehensive record of 124,000+ job postings listed in 2023 and 2024. . 🔰 Direct dataset download link: https://www.kaggle.com/api/v1/datasets/download/arshkon/linkedin-job-postings 📊 Additional information: File count not found Views: 126,000 Downloads: 53,100 📚 RELATED NOTEBOOKS: 1. "Decoding the Job Market: An In-depth Exploration | Upvotes: 84 URL: https://www.kaggle.com/code/pratul007/decoding-the-job-market-an-in-depth-exploration 2. LinkedIn Job Postings 2023 Data Analysis | Upvotes: 58 URL: https://www.kaggle.com/code/enricofindley/linkedin-job-postings-2023-data-analysis @Machine_learn

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🔹 Title: Forecasting Probability Distributions of Financial Returns with Deep Neural Networks 🔹 Publication Date: Published on Aug 26 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.18921 • PDF: https://arxiv.org/pdf/2508.18921 • Github: https://github.com/jmichankow/deep_learning_probability @Machine_learn

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Dataset Name: Real Life Violence Situations Dataset Basic Description: 1000 videos containing real street fight and 1000 video from other classes 🔴 Dataset Size: Download dataset as zip (4 GB) 🔰 Direct dataset download link: https://www.kaggle.com/api/v1/datasets/download/mohamedmustafa/real-life-violence-situations-dataset 1. Real Time Violence Detection | MobileNet Bi-LSTM | Upvotes: 424 URL: https://www.kaggle.com/code/abduulrahmankhalid/real-time-violence-detection-mobilenet-bi-lstm 2. Real life violence detection using InceptionV3 | Upvotes: 395 URL: https://www.kaggle.com/code/nandinibagga/real-life-violence-detection-using-inceptionv3 3. Real Life Violence Detection / KERAS-TENSORFLOW | Upvotes: 115 URL: https://www.kaggle.com/code/brsdincer/real-life-violence-detection-keras-tensorflow 4. Video Fights Dataset | Upvotes: 24 URL: https://www.kaggle.com/datasets/shreyj1729/cctv-fights-dataset @Machine_learn

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Repost from Papers